This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.
The success of antiretroviral therapy has led to an increase in life expectancy and an associated rise in the risk of cardiometabolic diseases (CMDs) among people living with HIV.
Our aim was to conduct a systematic review to synthesize the existing literature on the patterns of use and effects of mobile health (mHealth) interventions for improving treatment adherence and outcomes of care for CMD among people living with HIV.
A systematic search of multiple databases, including PubMed-MEDLINE, Embase, CINAHL, Scopus, Web of Science, African Journals online, ClinicalTrials.gov, and the World Health Organization Global Index Medicus of peer-reviewed articles, was conducted with no date or language restrictions. Unpublished reports on mHealth interventions for treatment adherence and outcomes of care for CMD among adults living with HIV were also included in this review. Studies were included if they had at least 1 component that used an mHealth intervention to address treatment adherence or 1 or more of the stated outcomes of care for CMD among people living with HIV.
Our search strategy yielded 1148 unique records. In total, 10 articles met the inclusion criteria and were included in this review. Of the 10 studies, only 4 had published results. The categories of mHealth interventions ranged from short messaging, telephone calls, and wearable devices to smartphone and desktop web-based mobile apps. Across the different categories of interventions, there were no clear patterns in terms of consistency in the use of a particular intervention, as most studies (9/10, 90%) assessed a combination of mHealth interventions. Short messaging and telephone calls were however the most common interventions. Half of the studies (5/10, 50%) reported on outcomes that were indirectly linked to CMD, and none of them provided reliable evidence for evaluating the effectiveness of mHealth interventions for treatment adherence and outcomes of care for CMD among people living with HIV.
Due to the limited number of studies and the heterogeneity of interventions and outcome measures in the studies, no definitive conclusions could be drawn on the patterns of use and effects of mHealth interventions for treatment adherence and outcomes of care for CMD among people living with HIV. We therefore recommend that future trials should focus on standardized outcomes for CMD. We also suggest that future studies should consider having a longer follow-up period in order to determine the long-term effects of mHealth interventions on CMD outcomes for people living with HIV.
PROSPERO International Prospective Register of Systematic Reviews CRD42018086940; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42018086940
Cardiometabolic diseases (CMDs) represent a huge threat to the global progress that has been achieved in reducing mortality and morbidity among people living with HIV [
The use of mobile technology, especially the use of mobile phones, has increased tremendously worldwide. The majority of the over 7 billion mobile phone users reside in low- and middle-income countries (LMICs), where the burden of HIV and AIDS is the heaviest [
Research on CMD among people living with HIV has increased significantly over the last 2 decades, and significant proportions of these studies have assessed the use of mobile health (mHealth) interventions to promote HIV care [
For this review, we followed the guidelines from the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) Statement [
To ensure that we captured all articles that described patterns of existing mHealth interventions for CMD outcomes, we did not limit our search by study type or design. We included studies that described any mHealth interventions if the following criteria were met: (1) the study was conducted among adults aged ≥18 years living with HIV; (2) the study involved the use of mobile phones or had any mHealth components embedded in its design; and (3) the study was designed to influence adherence to treatment or outcomes of care for 1 or more CMD (ie, hypertension, dyslipidemia, obesity, stroke, coronary heart disease, diabetes mellitus, and metabolic syndrome). Articles published in any language with English abstracts were eligible for inclusion.
A study was excluded if (1) it was an opinion piece, (2) it was a publication that lacked primary data, and (3) it had no explicit method description. In the case of duplicate publications of the same material in more than 1 journal or conference proceeding, the most complete and recent version was used.
The diagnosis of HIV was based on positive reports from screening tests or participants’ self-reported doctor diagnoses. The CMDs reviewed included hypertension, diabetes mellitus, dyslipidemia, obesity, stroke, coronary heart disease, and metabolic syndrome. This study was a systematic review for which no primary data were collected. The diagnosis of HIV and cardiometabolic outcomes were based on reports from the studies included in this review.
The outcome measures included differences in adherence to treatment or outcomes relating to any of the listed CMDs; differences in mortality due to the listed CMDs; differences in blood pressure, glycemic control, and blood lipid levels; and reductions in CMD risk or BMI waist circumference and waist-hip ratios.
With regard to mHealth, for the purpose of this review, we used the Global Observatory for eHealth definition, which defines mHealth as a medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, PDAs, and other wireless devices. mHealth involves the use and capitalization of a mobile phone’s core utility in terms of voice messaging services and SMSs as well as more complex functionalities and applications, including general packet radio services, third and fourth generation mobile telecommunications (3G and 4G systems), GPSs, and Bluetooth technology [
Electronic searches of the following databases were conducted from inception to September 2019: PubMed-MEDLINE, Embase, CINAHL, Scopus, Web of Science, Cochrane Central Register of Controlled Trials, Global Health (Elton B. Stephens Company), the Institute of Electrical and Electronics Engineers, African Journals online, the Association for Computing Machinery, World Health Organization (WHO) reports, ClinicalTrials.gov, The Pan African Clinical Trials Registry and mHealth alliance, and the WHO Global Index Medicus. The search terms used included HIV-related terms (eg,
We searched for additional relevant articles in the reference lists of the retrieved key articles and reviews. We contacted authors of included studies to acquire other data related to our outcomes of interest that may have been unpublished, informally published, or undergoing ongoing analysis. Principal investigators of registered clinical trials whose published results were not found within our search were contacted by email to share their results.
Search results were saved into EndNote (Clarivate Analytics) files by the librarian (LO). All EndNote files were deduplicated, collated, and transferred into Rayyan (Rayyan Systems Incorporated) [
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart. CMD: cardiometabolic disease.
The quality of included studies was assessed by using the Cochrane risk of bias assessment tool [
Our search yielded 1148 unique records, of which 1129 were excluded for the reasons specified in
The 10 included studies and their key study characteristics are summarized in
Summary of the key characteristics of included studies (N=10).
Study (author, year, country) | Study design and methods | Inclusion criteria | Interventions | Outcomes | Reported results |
Morillo-Verdugo et al [ |
Randomized controlled trial | Aged >35 years; on antiretroviral therapy with at least 1 drug prescribed for the treatment of hypertension, dyslipidemia, angina pectoris, cardiovascular prophylaxis, or type 2 diabetes mellitus; and at a moderate or high risk of cardiovascular disease | Periodic text messages on mobile phones | Cardiovascular risk index, smoking reduction, blood pressure control, and medication adherence | 20.7% of patients in the intervention group vs 12.5% of patients in the control group reduced their Framingham risk score from high/very high to moderate/low ( |
Anglada-Martinez et al [ |
Single-arm, prospective pre- and postintervention study | Patients on treatment for heart failure, hypertension, or dyslipidemia for >1 month and those aged >18 years | Medplan smartphone app and weekly motivational messages | Medication adherence, cholesterol, triglycerides, and blood pressure control | The proportion of missed doses decreased significantly for patients using the Medplan app ( |
Roos et al [ |
Randomized controlled trial | On antiretroviral therapy for >6 months, aged 20-65 years, ambulatory without assistive device, and had an elevated risk of ischemic heart disease | Pedometer, activity diary that included education materials and documents for self-monitoring, and 1 monthly cell phone SMS message for motivation | The pedometer step count of both groups improved significantly. | The pedometer step counts of the control and intervention groups improved significantly ( |
Zuniga et al [ |
1-group pre- and posttest design | Aged >18 years and had |
6-hour educational instruction implemented as 2 3-hour meetings followed byweekly telephone support calls for 6 weeks | Diabetes self-management skills and knowledge about HIV or diabetes | There was a 34% increase in diabetes self-management skills from pretest to posttest, but there were no changes in knowledge about HIV or diabetes. |
Grinspoon [ |
Randomized case control study | Aged 18 to 65 years and had 3 of the following 5 characteristics: (1) waist circumferences of >102 cm (40 in) for men and >88 cm (35 in) in women; (2) triglyceride levels of ≥150 mg/dL or current antilipolytic drug treatment; (3) high-density lipoprotein levels of <40 mg/dL for men and <50 mg/dL for women; (4) blood pressure of ≥130/85 mmHg or current antihypertensive drug treatment; and (5) fasting glucose level of ≥110 mg/dL | 1-time counseling session with nutrition staff at the baseline visit and monthly unscripted phone calls | Waist-hip ratios and cardiovascular indices (total cholesterol; low-density lipoprotein, high-density lipoprotein, and triglyceride cholesterol levels; blood pressure; cardiac enzymes; C-reactive protein; tissue plasminogen activator; plasminogen activator inhibitor; insulin; and glucose metabolism) | The results of the study have yet to be published. |
Jaggers et al [ |
Randomized controlled trial | Aged >18 years, had a sedentary lifestyle, had a viral load of >75 copies/mL, was capable of performing required exercise regimen, and had daily access to a telephone for approximately 10 months | Home-based physical activity intervention: The intervention included a 60-min, individual, face-to-face session; telephone counselling calls; and educational workbooks and pedometers for the self-monitoring of physical activity. | The effect of the intervention in terms of decreasing modifiable risk factors and increasing physical activity among people living with HIV and the effect of the intervention in terms of decreasing modifiable risk factors, such as fat distribution, blood lipids, and cardiorespiratory fitness outcomes, were assessed. | The findings of the study have yet to be published. |
Brooke [ |
Randomized controlled trial | People living with HIV | Personalized, automated, interactive mobile phone text message intervention | Physical activity and dietary assessments; polyunsaturated fatty acids, carotenoids, and other biomarkers in plasma; and total cholesterol, triglyceride, and high- and low-density cholesterol | The study is still ongoing. |
Dodson et al [ |
Cluster randomized controlled trial | Aged >30 years, was receiving care from a participating doctor, was not diagnosed with cardiovascular disease, and had not participated previously in an HIV-specific self- management or coaching program | Health map website for (1) routine clinic visits involving the sharing of health records with a doctor; (2) access to own health record and information from home; (3) access to telephone and web-based self-management support; and (4) access to a peer-moderated, web-based group chat program. | 10-year risk of nonfatal acute myocardial infarction or coronary heart disease death, as estimated by a Framingham Heart Study risk equation and the Positive and Active Engagement in Life Scale from the Health Education Impact Questionnaire | The findings of the study have yet to be published. |
Oduor et al [ |
Contextual user interviews | Patients living with HIV and hypertension | Integrated desktop and mobile app | Improved efficacy, safety, and personalization of medication prescription | Descriptive study |
Kengne [ |
Randomized controlled trial | Adult South Africans with comorbid HIV and hypertension | Automated text messaging | Mean difference in systolic and diastolic blood pressure at baseline and follow-up, uptake and adherence to blood pressure medications, mean change in lipid variables, and mean change in adiposity variables | The results of this study have yet to be published. |
All included studies had an mHealth component, although some studies used a multimethod mHealth intervention approach, wherein support telephone calls were combined with educational instructions [
Although studies had varied outcomes, most had treatment adherence (1/10, 10%) [
With regard to the function of mHealth interventions, 4 studies used mHealth for medication adherence purposes [
The majority of the studies (9/10, 90%) were conducted in hospital or clinic settings [
With regard to the effects of mHealth interventions on treatment adherence and cardiometabolic outcomes in the 4 studies with published results, 2 studies reported a decrease in cardiovascular risk [
Cochrane risk of bias assessment [
Among the 10 included studies, the assessment of bias was conducted in only the 4 studies that were completed (Anglada-Martinez et al [
Only 1 out of the 4 completed studies carried out blinding, and this was the blinding of the study personnel. In that study the research assistant performed all of the assessments and was blinded to the group allocation. The assessment forms were also coded to ensure anonymity, and the first author conducted the intervention [
The studies by Anglada-Martinez et al [
With regard to detection bias, the study by Anglada-Martinez et al [
In the study by Anglada-Martinez et al [
In the study by Morillo-Verdugo et al [
In the study by Roos et al [
In the study by Zuniga et al [
This paper presents a narrative synthesis of mHealth interventions for treatment adherence and outcomes of care for CMD among adults with HIV. A total of 10 studies met the inclusion criteria and were included in the review. The majority of studies included in this review were conducted in high-income countries (7/10, 70%) [
Based on our review, the categories of the interventions that were used in the studies ranged from short messaging and telephone calls to wearable devices and smartphone and desktop web-based mobile apps. However, the two most common interventions that were provided to people living with HIV were short messaging and telephone calls. This finding is similar to those from another systematic review on the impact of mHealth chronic disease management on treatment adherence and patient outcomes, which found that 40% of the studies included in their review had used short messaging to track medication adherence in patients with chronic diseases [
Overall, authors reported that the use of mHealth interventions for treatment adherence and outcomes of care for CMD among adults living with HIV was effective. However, studies varied widely in terms of research questions, target groups, study outcomes, and settings [
Most existing studies on mHealth interventions for people living with HIV have addressed ART adherence outcomes, and only a few have assessed mHealth interventions for CMD outcomes. In theory, the use of mHealth interventions to monitor treatment adherence and outcomes of care for both ART and CMD should make the process of care more efficient. However, research in this area is still very limited. This highlights the need to generate evidence to promote the use of integrated models of care for outcomes such as ART adherence and CMD outcomes. In addition, our findings from this review revealed that most studies did not report outcomes such as a reduction in the incidence of CMD, which should be the ultimate goal, given the increasing life expectancy of people living with HIV resulting from ART. However, this could be explained by the short follow-up periods that were used in these studies. We highlight the existing mHealth interventions that specifically target CMD outcomes among people living with HIV and draw attention to the gaps and opportunities in mHealth interventions for comorbid CMDs among people living with HIV. Furthermore, our review shows the paucity of well-designed RCTs in this research area. We also call attention to the disparities in the conduct of research on this topic. Globally, the WHO African region has been and remains to be the most severely affected by the HIV epidemic, as it accounts for more than two-thirds of the people living with HIV worldwide and nearly 3.7% adults (about 1 in every 25) living with HIV [
Although we purposefully used broad inclusion criteria to capture all studies evaluating any type of mHealth intervention for CMD among people living with HIV, we were limited by the low number of studies that met our inclusion criteria or reported on our predefined key outcomes. Furthermore, many of the studies that fit our inclusion criteria were old. Therefore, they may not have reflected the current state of the effectiveness of mHealth interventions. We also could not conduct a meta-analysis due to the heterogeneity of the included studies in terms of their methods and reported outcomes. However, it is important to point out that a number of clinical trials are underway, and their results can be incorporated in a follow-up review within the next few years. We recommend that future trials should focus on standardized outcomes for CMD to enable the conduction of a meta-analysis. We also suggest that future studies should consider using an integrated approach and a longer follow-up period in order to determine the long-term effects of mHealth interventions on outcomes of care.
Studies using mHealth interventions that specifically target CMD outcomes for people living with HIV are limited, particularly in Sub-Saharan Africa, where the burden of HIV is the greatest. In this review, although several of the mHealth interventions were found to be effective, there appears to be no clear pattern in the use of mHealth interventions for CMD outcomes. Short messaging was the most used intervention. More studies that assess the use and effectiveness of mHealth interventions other than short messaging, such as mobile apps and wearable health devices, are needed in this study area.
Search strings.
Data extraction template [<xref ref-type="bibr" rid="ref35">35</xref>-<xref ref-type="bibr" rid="ref38">38</xref>].
antiretroviral therapy
cardiometabolic disease
low- and middle-income country
mobile health
Preferred Reporting Items for Systematic reviews and Meta-Analyses
randomized controlled trial
World Health Organization
APK is supported by a European and Developing Countries Clinical Trials Partnership senior fellowship (grant TMA2017GSF-1962; Cardiometabolic Diseases Risk Evaluation and Reduction in African People Living with HIV Infection) through the South African Medical Research Council. OOO is supported by the Fogarty International Center of the National Institutes of Health under award number K43TW010704. The content of this review is solely the responsibility of the authors and does not necessarily represent the official views of the South African Medical Research Council or the National Institutes of Health.
None declared.